· Fine-Tune and Deploy Language Models: Optimize small language models (SLMs) for specific ERP-related tasks. Deploy models to cloud infrastructure (e.g., Azure, AWS) and on-premise environments ensuring scalability, security, and performance. Work with foundation models hosted on LLM providers.
· Build AI Agents & Autonomously Operating Systems: Design and implement AI agents capable of handling workflows, task automation, and adaptive decision-making across ERP modules (e.g., finance, inventory, CRM). Experience with frameworks like LangChain, AutoGPT, or similar is a strong plus.
· Traditional Machine Learning: Develop and maintain classical ML models for time series forecasting, anomaly detection, classification, and clustering to solve core business problems (e.g., fraud detection, demand forecasting, churn prediction). Strong experience with feature engineering, model evaluation, and A/B testing is required.
· Collaborate Across Teams: Work with product managers, software engineers, and UX designers to define AI use cases and integrate models seamlessly into production systems.
· Own the MLOps Lifecycle: Implement CI/CD pipelines for ML models, including version control, monitoring, and retraining strategies. Ensure responsible and ethical AI practices in model design and deployment.
· 5+ years of hands-on experience in data science, with a focus on machine learning and natural language processing
· Strong proficiency with Python, TensorFlow, PyTorch, Scikit-learn, HuggingFace, or similar ML/NLP libraries
· Proven experience with language models fine-tuning
· Experience deploying ML models to cloud infrastructure (Azure, AWS)
· Experience with AI agents, LLM orchestration, and tools such as LangChain, AutoGPT, or CrewAI
· Deep understanding of traditional ML techniques
· Experience with MLOps best practices, including model monitoring, pipelines, and reproducibility
· Strong communication and collaboration skills
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